Eddie Module Global Change

Eddie Module on global Change

Install libraries

Install libraries for use to examine data using tidyverse this only has to be done one time and a # can be put in front of the code to comment it out so it does not run in the future as now.

# install.packages("tidyverse")
# install.packages("readxl")
# install.packages("janitor")
# install.packages("lubridate")
# install.packages("broom")
# install.packages("patchwork")
# install.packages("plotly")

Load libraries

Load the libraries each time you run a script or program

library(tidyverse) # loads a lot of libraries in this one to add more functionality

Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ ggplot2 3.2.1     ✓ purrr   0.3.3
✓ tibble  2.1.3     ✓ dplyr   0.8.3
✓ tidyr   1.0.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.4.0
── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(readxl) # allows you to read in excel files
library(janitor) # cleans up column names and removes empty columns if wanted

Attaching package: ‘janitor’

The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test

library(lubridate) # allows easy conversion of varaibles to date format

Attaching package: ‘lubridate’

The following object is masked from ‘package:base’:

    date

library(broom) # cleans up output for easy presentation
library(scales) # works with the x and y scales on the plots

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor

library(patchwork) # allows you to plot several graphs on one page
library(plotly)

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  print.data.table     
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  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
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  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

turn off sci notation

options(scipen=999)

Read files

Recent global temperatures

global_temp.df <- read_excel("../data/climate_change_module_dataset.xls", 
                             sheet = "Global Temperature",
                             skip=4)

Global CO₂

loa_co2.df <- read_excel("../data/climate_change_module_dataset.xls", 
                             sheet = "Mauna Loa CO2",
                             skip=4)

Vostock temperatures

vostok_temp.df <- read_excel("../data/climate_change_module_dataset.xls", 
                            sheet = "Vostok Temp",
                            skip=4)

Vostock CO₂

vostok_co2.df <- read_excel("../data/climate_change_module_dataset.xls", 
                            sheet = "Vostok CO2",
                            skip=4)

Plot of global temperatures

Note this uses a different approach to explore the data The graph will be interactive

global_temp.plot <- global_temp.df %>%
                          ggplot(aes(year, temp_c)) +
                          geom_line()+
                          geom_point()
# global_temp.plot
ggplotly(global_temp.plot)

Plot of global tempreatures - subset of data

# Enter the years here
min_year_global = 1976
max_year_global =  1998

global_temp.df %>%
  filter(year >= min_year_global & year <= max_year_global) %>%
  summarize(time_difference_years = max_year_global - min_year_global,
           min_temp_c = min(temp_c, na.rm=TRUE), 
            max_temp_c = max(temp_c, na.rm=TRUE))

global_temp.plot <- global_temp.df %>%
  filter(year >= min_year_global & year <= max_year_global) %>%
  ggplot(aes(year, temp_c)) +
  geom_line()+
  geom_point()
# global_temp.plot
ggplotly(global_temp.plot)

Plot of Vostok tempreatures ——

vostok_temp.plot <- vostok_temp.df %>%
  ggplot(aes(ice_age_year_bp, temp_c)) +
  geom_line()+
  geom_point() +
  scale_x_continuous(label=comma)
# vostok_temp.plot
ggplotly(vostok_temp.plot)

Plot of Vostok tempreatures - subset

# Enter the years here
min_year_vostok = 231979
max_year_vostok =  237643

vostok_temp.df %>%
  filter(ice_age_year_bp >= min_year_vostok & ice_age_year_bp <= max_year_vostok) %>%
  summarize(time_difference_years = max_year_vostok - min_year_vostok,
            min_temp_c = min(temp_c, na.rm=TRUE), 
            max_temp_c = max(temp_c, na.rm=TRUE))

# plot of temp over time for sub area
vostok_temp.plot <- vostok_temp.df %>%
  filter(ice_age_year_bp >= min_year_vostok & ice_age_year_bp <= max_year_vostok) %>%
  ggplot(aes(ice_age_year_bp, temp_c)) +
  geom_line()+
  geom_point()
# vostok_temp.plot
ggplotly(vostok_temp.plot)

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